Content area
In this study, we aimed to enhance the accuracy of product quality inspection and counting in the manufacturing process by integrating image processing and human body detection algorithms. We employed the SIFT algorithm combined with traditional image comparison metrics such as SSIM, PSNR, and MSE to develop a defect detection system that is robust against variations in rotation and scale. Additionally, the YOLOv8 Pose algorithm was used to detect and correct errors in product counting caused by human interference on the load cell in real time. By applying the image differencing technique, we accurately calculated the unit weight of products and determined their total count. In our experiments conducted on products weighing over 1 kg, we achieved a high accuracy of 99.268%. The integration of our algorithms with the load-cell-based counting system demonstrates reliable real-time quality inspection and automated counting in manufacturing environments.
Details
Load cells;
Deep learning;
Defects;
Productivity;
Quality control;
Computer vision;
Automation;
Manufacturing;
Product image;
Image processing;
Data compression;
Efficiency;
Cameras;
Inspection;
Neural networks;
Algorithms;
Data collection;
Rotating bodies;
Image quality;
Real time;
Counting;
Cost control;
Inventory management;
Human error;
Inventory
